Covail
Difference in risk between prototype and Omicon vaccines
Summary: Between Pfz Prototype, Pfz Omicron, Mdn P, and Mdn O,
- Pfizer O has lower risk than the other three arms.
- Moderna P is the only arm in terms of BA4BA5 ID50 not being a correlate.
Risk is similar between Pfizer and Moderna in the prototype arms. Moderna P and O have similar risks, but Pfizer O has lower risk than P.
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ company, subset(dat.ocp,naive==1 & Trt==0))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## company, data = subset(dat.ocp, naive == 1 & Trt == 0))
##
## coef exp(coef) se(coef) z p
## companyPfz 0.4122 1.5101 0.3472 1.187 0.235
##
## Likelihood ratio test=1.35 on 1 df, p=0.2454
## n= 105, number of events= 36coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + company + Day15pseudoneutid50_BA.4.BA.5 + FOIstandardized, subset(dat.ocp,naive==1 & Trt==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + company + Day15pseudoneutid50_BA.4.BA.5 +
## FOIstandardized, data = subset(dat.ocp, naive == 1 &
## Trt == 1))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.4680 1.5969 0.1666 2.810 0.004958
## companyPfz -0.9440 0.3891 0.3578 -2.638 0.008329
## Day15pseudoneutid50_BA.4.BA.5 -0.7299 0.4819 0.2006 -3.639 0.000273
## FOIstandardized -0.1906 0.8264 0.2324 -0.820 0.412125
##
## Likelihood ratio test=27.65 on 4 df, p=1.469e-05
## n= 213, number of events= 57coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt, subset(dat.ocp,naive==1 & company=="Pfz"))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt, data = subset(dat.ocp, naive == 1 & company == "Pfz"))
##
## coef exp(coef) se(coef) z p
## Trt -1.238 0.290 0.422 -2.933 0.00336
##
## Likelihood ratio test=8.54 on 1 df, p=0.003482
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt, subset(dat.ocp,naive==1 & company=="Mdn"))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt, data = subset(dat.ocp, naive == 1 & company == "Mdn"))
##
## coef exp(coef) se(coef) z p
## Trt -0.02566 0.97466 0.25475 -0.101 0.92
##
## Likelihood ratio test=0.01 on 1 df, p=0.9199
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt * company, subset(dat.ocp, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * company, data = subset(dat.ocp, naive == 1))
##
## coef exp(coef) se(coef) z p
## Trt -0.0218 0.9784 0.2547 -0.086 0.9318
## companyPfz 0.4226 1.5259 0.3471 1.218 0.2234
## Trt:companyPfz -1.2309 0.2920 0.4917 -2.503 0.0123
##
## Likelihood ratio test=10.39 on 3 df, p=0.01553
## n= 318, number of events= 93coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt) * company, subset(dat.ocp, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * company, data = subset(dat.ocp, naive == 1))
##
## coef exp(coef) se(coef) z p
## I(1 - Trt) 0.0218 1.0220 0.2547 0.086 0.9318
## companyPfz -0.8083 0.4456 0.3483 -2.320 0.0203
## I(1 - Trt):companyPfz 1.2309 3.4242 0.4917 2.503 0.0123
##
## Likelihood ratio test=10.39 on 3 df, p=0.01553
## n= 318, number of events= 93Pfizer
OC v P is significant among the naive but not among the non-naive.
coxph(update(f, ~. + naive + Trt), dat.ocp.pfizer)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp.pfizer)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1126 0.8935 0.2984 -0.377 0.70591
## standardized_risk_score 0.2795 1.3224 0.2878 0.971 0.33154
## naive 0.8310 2.2955 0.4551 1.826 0.06787
## Trt -1.1399 0.3199 0.3749 -3.040 0.00236
##
## Likelihood ratio test=12.14 on 4 df, p=0.01631
## n= 151, number of events= 30coxph(update(f, ~. + Trt), subset(dat.ocp.pfizer, naive==0))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.pfizer,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.59531 1.81359 0.61480 0.968 0.333
## standardized_risk_score 0.04664 1.04775 0.52826 0.088 0.930
## Trt -0.52997 0.58862 0.77255 -0.686 0.493
##
## Likelihood ratio test=1.38 on 3 df, p=0.7098
## n= 53, number of events= 7coxph(update(f, ~. + Trt), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3243 0.7231 0.3318 -0.977 0.3285
## standardized_risk_score 0.4130 1.5113 0.3592 1.150 0.2503
## Trt -1.3346 0.2633 0.4359 -3.062 0.0022
##
## Likelihood ratio test=10.59 on 3 df, p=0.01415
## n= 98, number of events= 23coxph(update(f, ~. + naive + Trt), dat.ocp.moderna)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1298 0.8782 0.1923 -0.675 0.49956
## standardized_risk_score 0.2056 1.2283 0.1441 1.427 0.15360
## naive 1.0871 2.9657 0.3741 2.906 0.00367
## Trt -0.1180 0.8887 0.2388 -0.494 0.62124
##
## Likelihood ratio test=13.62 on 4 df, p=0.008612
## n= 284, number of events= 78coxph(update(f, ~. + Trt), subset(dat.ocp.moderna, naive==0))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.moderna,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.4871 0.6144 0.5445 -0.895 0.3710
## standardized_risk_score -0.4855 0.6154 0.2100 -2.312 0.0208
## Trt -0.5147 0.5977 0.7306 -0.705 0.4811
##
## Likelihood ratio test=6.87 on 3 df, p=0.07628
## n= 64, number of events= 8coxph(update(f, ~. + Trt), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.03082 0.96965 0.20833 -0.148 0.8824
## standardized_risk_score 0.45535 1.57672 0.16296 2.794 0.0052
## Trt -0.07904 0.92400 0.25598 -0.309 0.7575
##
## Likelihood ratio test=8.48 on 3 df, p=0.03705
## n= 220, number of events= 70coxph(update(f, ~. + Trt), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.03082 0.96965 0.20833 -0.148 0.8824
## standardized_risk_score 0.45535 1.57672 0.16296 2.794 0.0052
## Trt -0.07904 0.92400 0.25598 -0.309 0.7575
##
## Likelihood ratio test=8.48 on 3 df, p=0.03705
## n= 220, number of events= 70coxph(update(f, ~. + Trt), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3243 0.7231 0.3318 -0.977 0.3285
## standardized_risk_score 0.4130 1.5113 0.3592 1.150 0.2503
## Trt -1.3346 0.2633 0.4359 -3.062 0.0022
##
## Likelihood ratio test=10.59 on 3 df, p=0.01415
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z
## I(1 - Trt) 0.1259 1.1341 0.2626 0.479
## scale(standardized_risk_score) 0.4569 1.5792 0.1567 2.917
## I(1 - Trt):scale(standardized_risk_score) -0.3079 0.7350 0.2860 -1.077
## p
## I(1 - Trt) 0.63173
## scale(standardized_risk_score) 0.00354
## I(1 - Trt):scale(standardized_risk_score) 0.28163
##
## Likelihood ratio test=9.58 on 3 df, p=0.02251
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef) z
## I(1 - Trt) 1.334775 3.799141 0.439440 3.037
## scale(standardized_risk_score) 0.252246 1.286913 0.302894 0.833
## I(1 - Trt):scale(standardized_risk_score) -0.003043 0.996962 0.513319 -0.006
## p
## I(1 - Trt) 0.00239
## scale(standardized_risk_score) 0.40496
## I(1 - Trt):scale(standardized_risk_score) 0.99527
##
## Likelihood ratio test=9.64 on 3 df, p=0.02186
## n= 98, number of events= 23D15 marker
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score) + I(1-Trt)*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score) + I(1 - Trt) *
## scale(Day15pseudoneutid50_BA.4.BA.5), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef)
## I(1 - Trt) 0.1114 1.1178 0.2661
## scale(standardized_risk_score) 0.4149 1.5142 0.1504
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.4160 0.6597 0.1256
## I(1 - Trt):scale(standardized_risk_score) -0.2653 0.7670 0.2835
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 0.4191 1.5206 0.2328
## z p
## I(1 - Trt) 0.419 0.675561
## scale(standardized_risk_score) 2.759 0.005804
## scale(Day15pseudoneutid50_BA.4.BA.5) -3.311 0.000929
## I(1 - Trt):scale(standardized_risk_score) -0.936 0.349342
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 1.801 0.071746
##
## Likelihood ratio test=19.05 on 5 df, p=0.001884
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score) + I(1-Trt)*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score) + I(1 - Trt) *
## scale(Day15pseudoneutid50_BA.4.BA.5), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef)
## I(1 - Trt) 1.22319 3.39801 0.47177
## scale(standardized_risk_score) 0.25727 1.29339 0.28060
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.39649 0.67268 0.30594
## I(1 - Trt):scale(standardized_risk_score) -0.07131 0.93117 0.51280
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 0.08472 1.08841 0.48874
## z p
## I(1 - Trt) 2.593 0.00952
## scale(standardized_risk_score) 0.917 0.35923
## scale(Day15pseudoneutid50_BA.4.BA.5) -1.296 0.19498
## I(1 - Trt):scale(standardized_risk_score) -0.139 0.88940
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 0.173 0.86239
##
## Likelihood ratio test=11.86 on 5 df, p=0.03673
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt*scale(standardized_risk_score) + Trt*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * scale(standardized_risk_score) + Trt * scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.moderna, naive == 1))
##
## coef exp(coef) se(coef) z
## Trt -0.111365 0.894612 0.266089 -0.419
## scale(standardized_risk_score) 0.149640 1.161417 0.240360 0.623
## scale(Day15pseudoneutid50_BA.4.BA.5) 0.003167 1.003172 0.195995 0.016
## Trt:scale(standardized_risk_score) 0.265277 1.303791 0.283455 0.936
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) -0.419130 0.657619 0.232756 -1.801
## p
## Trt 0.6756
## scale(standardized_risk_score) 0.5336
## scale(Day15pseudoneutid50_BA.4.BA.5) 0.9871
## Trt:scale(standardized_risk_score) 0.3493
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) 0.0717
##
## Likelihood ratio test=19.05 on 5 df, p=0.001884
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt*scale(standardized_risk_score) + Trt*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * scale(standardized_risk_score) + Trt * scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.pfizer, naive == 1))
##
## coef exp(coef) se(coef) z
## Trt -1.22319 0.29429 0.47177 -2.593
## scale(standardized_risk_score) 0.18595 1.20437 0.42922 0.433
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.31177 0.73215 0.38065 -0.819
## Trt:scale(standardized_risk_score) 0.07131 1.07392 0.51280 0.139
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) -0.08472 0.91877 0.48874 -0.173
## p
## Trt 0.00952
## scale(standardized_risk_score) 0.66484
## scale(Day15pseudoneutid50_BA.4.BA.5) 0.41276
## Trt:scale(standardized_risk_score) 0.88940
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) 0.86239
##
## Likelihood ratio test=11.86 on 5 df, p=0.03673
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt*scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.moderna, naive == 1))
##
## coef exp(coef) se(coef) z p
## Trt 0.01104 1.01111 0.26824 0.041 0.96716
## scale(standardized_risk_score) 0.19188 1.21152 0.23255 0.825 0.40932
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.27822 0.75713 0.10780 -2.581 0.00986
## Trt:scale(standardized_risk_score) 0.23881 1.26974 0.27919 0.855 0.39234
##
## Likelihood ratio test=15.7 on 4 df, p=0.003451
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt+scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt + scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.pfizer, naive == 1))
##
## coef exp(coef) se(coef) z p
## Trt -1.1912 0.3039 0.4390 -2.713 0.00666
## scale(standardized_risk_score) 0.2285 1.2567 0.2312 0.988 0.32313
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.3558 0.7006 0.2317 -1.536 0.12463
##
## Likelihood ratio test=11.81 on 3 df, p=0.008078
## n= 98, number of events= 23BA4BA5-specific ID50 is significant correlate but not ancestral ID50.
coxph(update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.37008 0.69068 0.32778 -1.129 0.259
## standardized_risk_score 0.46192 1.58712 0.35856 1.288 0.198
## Trt -1.45196 0.23411 1.39782 -1.039 0.299
## Bpseudoneutid50_BA.4.BA.5 -0.76476 0.46545 0.51612 -1.482 0.138
## Trt:Bpseudoneutid50_BA.4.BA.5 0.02565 1.02598 0.75166 0.034 0.973
##
## Likelihood ratio test=14.86 on 5 df, p=0.011
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3707 0.6903 0.3275 -1.132 0.25778
## standardized_risk_score 0.4619 1.5871 0.3584 1.289 0.19754
## Trt -1.4067 0.2449 0.4402 -3.196 0.00139
## Bpseudoneutid50_BA.4.BA.5 -0.7528 0.4711 0.3771 -1.996 0.04593
##
## Likelihood ratio test=14.85 on 4 df, p=0.005012
## n= 98, number of events= 23coxph(update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3928 0.6751 0.3376 -1.163 0.245
## standardized_risk_score 0.4077 1.5033 0.3387 1.204 0.229
## Trt -0.5003 0.6063 2.3862 -0.210 0.834
## Day15pseudoneutid50_BA.4.BA.5 -0.5345 0.5860 0.6290 -0.850 0.395
## Trt:Day15pseudoneutid50_BA.4.BA.5 -0.2392 0.7873 0.8374 -0.286 0.775
##
## Likelihood ratio test=13.19 on 5 df, p=0.0217
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3846 0.6807 0.3365 -1.143 0.25298
## standardized_risk_score 0.3880 1.4741 0.3346 1.160 0.24613
## Trt -1.1710 0.3101 0.4380 -2.673 0.00751
## Day15pseudoneutid50_BA.4.BA.5 -0.6690 0.5122 0.4026 -1.661 0.09662
##
## Likelihood ratio test=13.1 on 4 df, p=0.01078
## n= 98, number of events= 23coxph(update(f, ~. + Trt * Bpseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3129 0.7313 0.3325 -0.941 0.347
## standardized_risk_score 0.3846 1.4690 0.3593 1.070 0.284
## Trt -2.5917 0.0749 3.0261 -0.856 0.392
## Bpseudoneutid50_D614G -0.7526 0.4711 0.6990 -1.077 0.282
## Trt:Bpseudoneutid50_D614G 0.3747 1.4545 0.9066 0.413 0.679
##
## Likelihood ratio test=12.25 on 5 df, p=0.03156
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3433 0.7094 0.3265 -1.051 0.29307
## standardized_risk_score 0.4042 1.4981 0.3542 1.141 0.25378
## Trt -1.3536 0.2583 0.4357 -3.106 0.00189
## Bpseudoneutid50_D614G -0.5220 0.5934 0.4159 -1.255 0.20949
##
## Likelihood ratio test=12.07 on 4 df, p=0.01682
## n= 98, number of events= 23coxph(update(f, ~. + Trt * Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.34880 0.70554 0.33352 -1.046 0.296
## standardized_risk_score 0.37937 1.46136 0.33824 1.122 0.262
## Trt 2.10109 8.17504 5.09592 0.412 0.680
## Day15pseudoneutid50_D614G 0.06733 1.06965 0.98947 0.068 0.946
## Trt:Day15pseudoneutid50_D614G -0.81890 0.44092 1.22190 -0.670 0.503
##
## Likelihood ratio test=11.66 on 5 df, p=0.03977
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3568 0.6999 0.3320 -1.075 0.28252
## standardized_risk_score 0.3775 1.4586 0.3485 1.083 0.27878
## Trt -1.3000 0.2725 0.4364 -2.979 0.00289
## Day15pseudoneutid50_D614G -0.4664 0.6273 0.5907 -0.789 0.42983
##
## Likelihood ratio test=11.21 on 4 df, p=0.02432
## n= 98, number of events= 23Distribution of D15 BA4BA5-specific ID50 is different between P and OC
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_BA.4.BA.5~Trt, dat.1, test="w")
myboxplot(Day15pseudoneutid50_BA.4.BA.5~Trt, dat.1, test="w")
Additional marker models
coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3455 0.7079 0.3285 -1.052 0.29297
## standardized_risk_score 0.3851 1.4697 0.3798 1.014 0.31063
## Trt -1.3295 0.2646 0.4678 -2.842 0.00449
## Bpseudoneutid50_D614G -0.4862 0.6150 0.4908 -0.991 0.32187
## Day15pseudoneutid50_D614G 0.7199 2.0541 9.1491 0.079 0.93729
## I(Day15pseudoneutid50_D614G^2) -0.0983 0.9064 1.0962 -0.090 0.92855
##
## Likelihood ratio test=12.1 on 6 df, p=0.05974
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G * Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G *
## Day15pseudoneutid50_D614G), data = dat.1)
##
## coef exp(coef) se(coef)
## FOIstandardized -0.3629 0.6956 0.3266
## standardized_risk_score 0.3633 1.4380 0.3727
## Trt -1.2999 0.2725 0.4475
## Bpseudoneutid50_D614G 2.9004 18.1816 6.1345
## Day15pseudoneutid50_D614G 2.4931 12.0987 4.7804
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -0.8016 0.4486 1.4502
## z p
## FOIstandardized -1.111 0.26650
## standardized_risk_score 0.975 0.32977
## Trt -2.905 0.00368
## Bpseudoneutid50_D614G 0.473 0.63635
## Day15pseudoneutid50_D614G 0.522 0.60200
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -0.553 0.58043
##
## Likelihood ratio test=12.44 on 6 df, p=0.05286
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G), data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.34752 0.70644 0.32782 -1.060 0.28910
## standardized_risk_score 0.39723 1.48770 0.35421 1.121 0.26210
## Trt -1.34393 0.26082 0.44034 -3.052 0.00227
## Bpseudoneutid50_D614G -0.48573 0.61525 0.49278 -0.986 0.32428
## Day15pseudoneutid50_D614G -0.09788 0.90675 0.68958 -0.142 0.88712
##
## Likelihood ratio test=12.09 on 5 df, p=0.03353
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3433 0.7094 0.3265 -1.051 0.29307
## standardized_risk_score 0.4042 1.4981 0.3542 1.141 0.25378
## Trt -1.3536 0.2583 0.4357 -3.106 0.00189
## Bpseudoneutid50_D614G -0.5220 0.5934 0.4159 -1.255 0.20949
##
## Likelihood ratio test=12.07 on 4 df, p=0.01682
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3568 0.6999 0.3320 -1.075 0.28252
## standardized_risk_score 0.3775 1.4586 0.3485 1.083 0.27878
## Trt -1.3000 0.2725 0.4364 -2.979 0.00289
## Day15pseudoneutid50_D614G -0.4664 0.6273 0.5907 -0.789 0.42983
##
## Likelihood ratio test=11.21 on 4 df, p=0.02432
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.39082 0.67650 0.33397 -1.170 0.24192
## standardized_risk_score 0.44598 1.56201 0.35217 1.266 0.20538
## Trt -1.32656 0.26539 0.48196 -2.752 0.00592
## Bpseudoneutid50_BA.4.BA.5 -0.63623 0.52928 0.44876 -1.418 0.15626
## Day15pseudoneutid50_BA.4.BA.5 -0.46895 0.62566 2.24326 -0.209 0.83441
## I(Day15pseudoneutid50_BA.4.BA.5^2) 0.03931 1.04009 0.41458 0.095 0.92446
##
## Likelihood ratio test=15.12 on 6 df, p=0.01931
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 * Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 *
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1)
##
## coef exp(coef)
## FOIstandardized -0.3963 0.6728
## standardized_risk_score 0.4624 1.5879
## Trt -1.3413 0.2615
## Bpseudoneutid50_BA.4.BA.5 -1.5970 0.2025
## Day15pseudoneutid50_BA.4.BA.5 -0.7192 0.4871
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.3065 1.3587
## se(coef) z p
## FOIstandardized 0.3337 -1.188 0.23499
## standardized_risk_score 0.3541 1.306 0.19161
## Trt 0.4739 -2.830 0.00465
## Bpseudoneutid50_BA.4.BA.5 2.8898 -0.553 0.58053
## Day15pseudoneutid50_BA.4.BA.5 1.4386 -0.500 0.61712
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.9028 0.340 0.73419
##
## Likelihood ratio test=15.23 on 6 df, p=0.01856
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3932 0.6749 0.3327 -1.182 0.2373
## standardized_risk_score 0.4418 1.5555 0.3494 1.264 0.2061
## Trt -1.3153 0.2684 0.4664 -2.820 0.0048
## Bpseudoneutid50_BA.4.BA.5 -0.6318 0.5316 0.4472 -1.413 0.1577
## Day15pseudoneutid50_BA.4.BA.5 -0.2616 0.7698 0.5056 -0.517 0.6049
##
## Likelihood ratio test=15.12 on 5 df, p=0.009877
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3707 0.6903 0.3275 -1.132 0.25778
## standardized_risk_score 0.4619 1.5871 0.3584 1.289 0.19754
## Trt -1.4067 0.2449 0.4402 -3.196 0.00139
## Bpseudoneutid50_BA.4.BA.5 -0.7528 0.4711 0.3771 -1.996 0.04593
##
## Likelihood ratio test=14.85 on 4 df, p=0.005012
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3846 0.6807 0.3365 -1.143 0.25298
## standardized_risk_score 0.3880 1.4741 0.3346 1.160 0.24613
## Trt -1.1710 0.3101 0.4380 -2.673 0.00751
## Day15pseudoneutid50_BA.4.BA.5 -0.6690 0.5122 0.4026 -1.661 0.09662
##
## Likelihood ratio test=13.1 on 4 df, p=0.01078
## n= 98, number of events= 23Moderna
No treatment effect in either naive or non-naive.
coxph(update(f, ~. + naive + Trt), dat.ocp.moderna)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1298 0.8782 0.1923 -0.675 0.49956
## standardized_risk_score 0.2056 1.2283 0.1441 1.427 0.15360
## naive 1.0871 2.9657 0.3741 2.906 0.00367
## Trt -0.1180 0.8887 0.2388 -0.494 0.62124
##
## Likelihood ratio test=13.62 on 4 df, p=0.008612
## n= 284, number of events= 78coxph(update(f, ~. + naive + Trt), subset(dat.ocp.moderna, naive==0))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp.moderna,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.4871 0.6144 0.5445 -0.895 0.3710
## standardized_risk_score -0.4855 0.6154 0.2100 -2.312 0.0208
## naive NA NA 0.0000 NA NA
## Trt -0.5147 0.5977 0.7306 -0.705 0.4811
##
## Likelihood ratio test=6.87 on 3 df, p=0.07628
## n= 64, number of events= 8coxph(update(f, ~. + naive + Trt), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.03082 0.96965 0.20833 -0.148 0.8824
## standardized_risk_score 0.45535 1.57672 0.16296 2.794 0.0052
## naive NA NA 0.00000 NA NA
## Trt -0.07904 0.92400 0.25598 -0.309 0.7575
##
## Likelihood ratio test=8.48 on 3 df, p=0.03705
## n= 220, number of events= 70Distribution of D15 BA4BA5-specific ID50 is different between P and OC
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_BA.4.BA.5~Trt, dat.1.moderna, test="w")
myboxplot(Day15pseudoneutid50_BA.4.BA.5~Trt, dat.1.moderna, test="w")
Among naive, there is significant interaction between Trt and D1 BA4BA5 ID50
coxph(update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.10876 0.89694 0.21194 -0.513 0.60782
## standardized_risk_score 0.51757 1.67794 0.17018 3.041 0.00236
## Trt 1.43400 4.19546 0.80039 1.792 0.07319
## Bpseudoneutid50_BA.4.BA.5 -0.03369 0.96687 0.31280 -0.108 0.91422
## Trt:Bpseudoneutid50_BA.4.BA.5 -0.78765 0.45491 0.38980 -2.021 0.04332
##
## Likelihood ratio test=22.09 on 5 df, p=0.0005028
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.08466 0.91883 0.21020 -0.403 0.6871
## standardized_risk_score 0.52343 1.68781 0.16853 3.106 0.0019
## Trt -0.05426 0.94719 0.25656 -0.211 0.8325
## Bpseudoneutid50_BA.4.BA.5 -0.55763 0.57256 0.18591 -3.000 0.0027
##
## Likelihood ratio test=18.02 on 4 df, p=0.001224
## n= 220, number of events= 70coxph(update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.063658 0.938326 0.208384 -0.305 0.76000
## standardized_risk_score 0.419372 1.521006 0.158592 2.644 0.00818
## Trt 2.167095 8.732879 1.238928 1.749 0.08026
## Day15pseudoneutid50_BA.4.BA.5 -0.006817 0.993206 0.346598 -0.020 0.98431
## Trt:Day15pseudoneutid50_BA.4.BA.5 -0.725744 0.483964 0.409206 -1.774 0.07614
##
## Likelihood ratio test=18.29 on 5 df, p=0.002603
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.04024 0.96056 0.20792 -0.194 0.84655
## standardized_risk_score 0.44264 1.55682 0.15841 2.794 0.00520
## Trt 0.05208 1.05347 0.26166 0.199 0.84222
## Day15pseudoneutid50_BA.4.BA.5 -0.49862 0.60737 0.18590 -2.682 0.00731
##
## Likelihood ratio test=15.02 on 4 df, p=0.004658
## n= 220, number of events= 70coxph(update(f, ~. + Trt * Bpseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.01409 0.98600 0.21342 -0.066 0.94734
## standardized_risk_score 0.48600 1.62579 0.16721 2.906 0.00366
## Trt 1.57583 4.83477 1.56471 1.007 0.31388
## Bpseudoneutid50_D614G -0.22904 0.79530 0.40054 -0.572 0.56745
## Trt:Bpseudoneutid50_D614G -0.48992 0.61268 0.46328 -1.058 0.29028
##
## Likelihood ratio test=17.22 on 5 df, p=0.004097
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.005631 1.005647 0.211626 0.027 0.97877
## standardized_risk_score 0.489978 1.632280 0.166749 2.938 0.00330
## Trt -0.042098 0.958776 0.256407 -0.164 0.86959
## Bpseudoneutid50_D614G -0.591856 0.553299 0.210497 -2.812 0.00493
##
## Likelihood ratio test=16.12 on 4 df, p=0.002862
## n= 220, number of events= 70coxph(update(f, ~. + Trt * Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.04257 0.95833 0.21104 -0.202 0.84015
## standardized_risk_score 0.42967 1.53675 0.16333 2.631 0.00852
## Trt 2.75942 15.79061 2.68283 1.029 0.30369
## Day15pseudoneutid50_D614G -0.33609 0.71456 0.52728 -0.637 0.52386
## Trt:Day15pseudoneutid50_D614G -0.67350 0.50992 0.63901 -1.054 0.29190
##
## Likelihood ratio test=16.55 on 5 df, p=0.005445
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.02905 0.97137 0.20991 -0.138 0.88992
## standardized_risk_score 0.43341 1.54250 0.16353 2.650 0.00804
## Trt -0.04242 0.95847 0.25644 -0.165 0.86862
## Day15pseudoneutid50_D614G -0.78976 0.45395 0.29689 -2.660 0.00781
##
## Likelihood ratio test=15.42 on 4 df, p=0.003899
## n= 220, number of events= 70Additional marker models
coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -5.814e-03 9.942e-01 2.114e-01 -0.028 0.9781
## standardized_risk_score 4.733e-01 1.605e+00 1.694e-01 2.794 0.0052
## Trt -3.537e-02 9.653e-01 2.567e-01 -0.138 0.8904
## Bpseudoneutid50_D614G -4.341e-01 6.478e-01 2.870e-01 -1.513 0.1304
## Day15pseudoneutid50_D614G 9.463e+00 1.287e+04 6.180e+00 1.531 0.1257
## I(Day15pseudoneutid50_D614G^2) -1.195e+00 3.028e-01 7.478e-01 -1.598 0.1101
##
## Likelihood ratio test=20.2 on 6 df, p=0.00255
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G * Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G *
## Day15pseudoneutid50_D614G), data = dat.1.moderna)
##
## coef exp(coef) se(coef)
## FOIstandardized -0.02009 0.98011 0.21239
## standardized_risk_score 0.47970 1.61559 0.16983
## Trt -0.04103 0.95980 0.25641
## Bpseudoneutid50_D614G 3.60636 36.83171 2.59976
## Day15pseudoneutid50_D614G 2.52712 12.51740 1.94240
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -0.94366 0.38920 0.61038
## z p
## FOIstandardized -0.095 0.92463
## standardized_risk_score 2.825 0.00473
## Trt -0.160 0.87285
## Bpseudoneutid50_D614G 1.387 0.16538
## Day15pseudoneutid50_D614G 1.301 0.19325
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -1.546 0.12210
##
## Likelihood ratio test=19.83 on 6 df, p=0.002965
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G), data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.007339 0.992688 0.211648 -0.035 0.9723
## standardized_risk_score 0.462311 1.587738 0.167240 2.764 0.0057
## Trt -0.031277 0.969207 0.256769 -0.122 0.9030
## Bpseudoneutid50_D614G -0.395893 0.673078 0.293522 -1.349 0.1774
## Day15pseudoneutid50_D614G -0.406311 0.666103 0.404493 -1.004 0.3151
##
## Likelihood ratio test=17.16 on 5 df, p=0.004213
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.005631 1.005647 0.211626 0.027 0.97877
## standardized_risk_score 0.489978 1.632280 0.166749 2.938 0.00330
## Trt -0.042098 0.958776 0.256407 -0.164 0.86959
## Bpseudoneutid50_D614G -0.591856 0.553299 0.210497 -2.812 0.00493
##
## Likelihood ratio test=16.12 on 4 df, p=0.002862
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.02905 0.97137 0.20991 -0.138 0.88992
## standardized_risk_score 0.43341 1.54250 0.16353 2.650 0.00804
## Trt -0.04242 0.95847 0.25644 -0.165 0.86862
## Day15pseudoneutid50_D614G -0.78976 0.45395 0.29689 -2.660 0.00781
##
## Likelihood ratio test=15.42 on 4 df, p=0.003899
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.16507 0.84784 0.21337 -0.774 0.43916
## standardized_risk_score 0.45693 1.57922 0.16953 2.695 0.00703
## Trt 0.09869 1.10373 0.26505 0.372 0.70963
## Bpseudoneutid50_BA.4.BA.5 -0.18224 0.83340 0.24952 -0.730 0.46517
## Day15pseudoneutid50_BA.4.BA.5 4.87481 130.94983 1.87375 2.602 0.00928
## I(Day15pseudoneutid50_BA.4.BA.5^2) -0.97889 0.37573 0.34700 -2.821 0.00479
##
## Likelihood ratio test=29.29 on 6 df, p=5.372e-05
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 * Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 *
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1.moderna)
##
## coef exp(coef)
## FOIstandardized -0.13156 0.87673
## standardized_risk_score 0.45336 1.57359
## Trt 0.04978 1.05104
## Bpseudoneutid50_BA.4.BA.5 5.66331 288.10120
## Day15pseudoneutid50_BA.4.BA.5 2.57487 13.12966
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 -1.87482 0.15338
## se(coef) z
## FOIstandardized 0.21151 -0.622
## standardized_risk_score 0.16885 2.685
## Trt 0.26683 0.187
## Bpseudoneutid50_BA.4.BA.5 1.70177 3.328
## Day15pseudoneutid50_BA.4.BA.5 0.84488 3.048
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.53345 -3.515
## p
## FOIstandardized 0.533957
## standardized_risk_score 0.007252
## Trt 0.851993
## Bpseudoneutid50_BA.4.BA.5 0.000875
## Day15pseudoneutid50_BA.4.BA.5 0.002307
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.000441
##
## Likelihood ratio test=32.41 on 6 df, p=1.363e-05
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.076829 0.926048 0.209936 -0.366 0.71439
## standardized_risk_score 0.501798 1.651689 0.167333 2.999 0.00271
## Trt -0.002618 0.997386 0.264350 -0.010 0.99210
## Bpseudoneutid50_BA.4.BA.5 -0.445376 0.640583 0.233371 -1.908 0.05633
## Day15pseudoneutid50_BA.4.BA.5 -0.201700 0.817340 0.246766 -0.817 0.41371
##
## Likelihood ratio test=18.67 on 5 df, p=0.002214
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.08466 0.91883 0.21020 -0.403 0.6871
## standardized_risk_score 0.52343 1.68781 0.16853 3.106 0.0019
## Trt -0.05426 0.94719 0.25656 -0.211 0.8325
## Bpseudoneutid50_BA.4.BA.5 -0.55763 0.57256 0.18591 -3.000 0.0027
##
## Likelihood ratio test=18.02 on 4 df, p=0.001224
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.04024 0.96056 0.20792 -0.194 0.84655
## standardized_risk_score 0.44264 1.55682 0.15841 2.794 0.00520
## Trt 0.05208 1.05347 0.26166 0.199 0.84222
## Day15pseudoneutid50_BA.4.BA.5 -0.49862 0.60737 0.18590 -2.682 0.00731
##
## Likelihood ratio test=15.02 on 4 df, p=0.004658
## n= 220, number of events= 70coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.1.moderna, Trt==1))## Call:
## coxph(formula = update(f, ~. + Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.1.moderna, Trt == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1627 0.8498 0.2699 -0.603 0.546488
## standardized_risk_score 0.4981 1.6456 0.1865 2.670 0.007581
## Day15pseudoneutid50_BA.4.BA.5 -0.7579 0.4686 0.2176 -3.484 0.000495
##
## Likelihood ratio test=19.88 on 3 df, p=0.0001794
## n= 145, number of events= 47coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.1.moderna, Trt==1))## Call:
## coxph(formula = update(f, ~. + Day15pseudoneutid50_D614G), data = subset(dat.1.moderna,
## Trt == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1406 0.8688 0.2749 -0.512 0.60893
## standardized_risk_score 0.5205 1.6829 0.1945 2.676 0.00745
## Day15pseudoneutid50_D614G -1.0358 0.3549 0.3641 -2.845 0.00444
##
## Likelihood ratio test=17.49 on 3 df, p=0.000561
## n= 145, number of events= 47Putting Pfizer and Moderna side by side
Omicron-specific antibodies are similar at D1, rise at D15, and more so when vaccines contain Omicron strains.
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_BA.4.BA.5~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
myboxplot(Day15pseudoneutid50_BA.4.BA.5~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
Ancestral antibodies are not quite similar at D1, rise at D15, but not more so when vaccines contain Omicron strains.
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_D614G~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
myboxplot(Day15pseudoneutid50_D614G~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
fits=list()
fits[["Pfz,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0))
fits[["Pfz,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==1))
fits[["Mdn,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==0))
fits[["Mdn,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==1))
tab = getFormattedSummary(fits, robust=F, type=5)
tab## Pfz,P Pfz,O Mdn,P Mdn,O
## FOIstandardized "-0.49" "-0.22" "0.19" "-0.14"
## standardized_risk_score "0.36" "0.36" "0.15" "0.52**"
## Day15pseudoneutid50_D614G "-0.15" "-0.70" "-0.37" "-1.04**"Are risk scores distribution different?
myboxplot(standardized_risk_score ~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score*Trt, subset(dat.ocp, naive==1 & company=="Mdn"))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score * Trt, data = subset(dat.ocp, naive ==
## 1 & company == "Mdn"))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.1836 1.2015 0.2949 0.623 0.534
## Trt -0.2121 0.8089 0.2839 -0.747 0.455
## standardized_risk_score:Trt 0.3793 1.4613 0.3523 1.077 0.282
##
## Likelihood ratio test=9.58 on 3 df, p=0.02251
## n= 220, number of events= 70BA4BA5 marker. Three out of the four effect sizes are comparable to each other, but in the Moderna prototype arm there is no association between BA4BA5 marker and risk. This is quite intriguing - if we view each column above as a correlates analysis, we have three positive results and one negative result.
fits=list()
fits[["Pfz,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0))
fits[["Pfz,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==1))
fits[["Mdn,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==0))
fits[["Mdn,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==1))
tab = getFormattedSummary(fits, robust=F, type=5)
tab## Pfz,P Pfz,O Mdn,P Mdn,O
## FOIstandardized "-0.51" "-0.29" "0.17" "-0.16"
## standardized_risk_score "0.25" "0.44" "0.16" "0.50**"
## Day15pseudoneutid50_BA.4.BA.5 "-0.62" "-0.75" "0.00" "-0.76**"Correlation between ancestral and BA4BA5 ID50s in Pfz,P and Pfz,O separately
par(mfrow=c(1,2))
lim=c(2.5,5.3)
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0), asp=1, xlim=lim, ylim=lim, main="Pfz, Prototype")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==1), asp=1, xlim=lim, ylim=lim, main="Pfz, Omicron")
Risk models
ID50 Score
dat$D1_id50=scale(dat$Bpseudoneutid50_MDW,scale=F)
dat$Day15_id50=scale(dat$Day15pseudoneutid50_MDW,scale=F)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ FOIstandardized + standardized_risk_score + naive + Day15_id50, dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## FOIstandardized + standardized_risk_score + naive + Day15_id50,
## data = dat)
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.001851 1.001853 0.082550 0.022 0.98211
## standardized_risk_score 0.220804 1.247079 0.084345 2.618 0.00885
## naive 1.054369 2.870165 0.210874 5.000 5.73e-07
## Day15_id50 -0.371085 0.689986 0.134860 -2.752 0.00593
##
## Likelihood ratio test=77.26 on 4 df, p=6.61e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24172 1.27343 0.08176 2.956 0.003112
## naive 0.66527 1.94501 0.22308 2.982 0.002862
## D1_id50 -0.44490 0.64089 0.16269 -2.735 0.006245
## Day15_id50 -0.49043 0.61236 0.25055 -1.957 0.050294
## I(Day15_id50^2) -0.98915 0.37189 0.27479 -3.600 0.000319
##
## Likelihood ratio test=103.4 on 5 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*(D1_id50 + Day15_id50 + I(Day15_id50^2)), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * (D1_id50 + Day15_id50 +
## I(Day15_id50^2)), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.23644 1.26673 0.08174 2.892 0.00382
## naive 0.56240 1.75488 0.22961 2.449 0.01431
## D1_id50 -0.91307 0.40129 0.36906 -2.474 0.01336
## Day15_id50 -0.76230 0.46659 0.56949 -1.339 0.18071
## I(Day15_id50^2) -0.75581 0.46963 0.46669 -1.620 0.10534
## naive:D1_id50 0.56603 1.76126 0.40890 1.384 0.16628
## naive:Day15_id50 0.33588 1.39917 0.63396 0.530 0.59624
## naive:I(Day15_id50^2) -0.13493 0.87377 0.55767 -0.242 0.80881
##
## Likelihood ratio test=108 on 8 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.23504 1.26496 0.08166 2.878 0.003998
## naive 0.53717 1.71115 0.21176 2.537 0.011192
## D1_id50 -1.02753 0.35789 0.30465 -3.373 0.000744
## Day15_id50 -0.47621 0.62113 0.24694 -1.928 0.053804
## I(Day15_id50^2) -0.88168 0.41409 0.26717 -3.300 0.000966
## naive:D1_id50 0.70604 2.02595 0.32593 2.166 0.030292
##
## Likelihood ratio test=107.7 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + naive*Day15_id50 + naive*I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + naive * Day15_id50 +
## naive * I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24273 1.27472 0.08174 2.969 0.00298
## naive 0.63995 1.89639 0.23269 2.750 0.00596
## D1_id50 -0.44191 0.64281 0.16069 -2.750 0.00596
## Day15_id50 -1.19176 0.30368 0.48297 -2.468 0.01360
## I(Day15_id50^2) -0.75693 0.46911 0.45572 -1.661 0.09673
## naive:Day15_id50 0.84626 2.33092 0.51659 1.638 0.10139
## naive:I(Day15_id50^2) -0.14280 0.86693 0.54910 -0.260 0.79482
##
## Likelihood ratio test=106.2 on 7 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + Day15_id50 + naive*I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + Day15_id50 +
## naive * I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24081 1.27228 0.08177 2.945 0.00323
## naive 0.70910 2.03216 0.24324 2.915 0.00355
## D1_id50 -0.44461 0.64108 0.16272 -2.732 0.00629
## Day15_id50 -0.52303 0.59272 0.26075 -2.006 0.04487
## I(Day15_id50^2) -0.70683 0.49321 0.62738 -1.127 0.25990
## naive:I(Day15_id50^2) -0.34038 0.71150 0.71376 -0.477 0.63345
##
## Likelihood ratio test=103.6 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + naive*Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + naive * Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24300 1.27507 0.08171 2.974 0.00294
## naive 0.61857 1.85626 0.21646 2.858 0.00427
## D1_id50 -0.44083 0.64350 0.16055 -2.746 0.00604
## Day15_id50 -1.21649 0.29627 0.48543 -2.506 0.01221
## I(Day15_id50^2) -0.86024 0.42306 0.26191 -3.285 0.00102
## naive:Day15_id50 0.88834 2.43109 0.50261 1.767 0.07715
##
## Likelihood ratio test=106.2 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213ID50 BA4BA5
dat$D1_id50=scale(dat$Bpseudoneutid50_BA.4.BA.5,scale=F)
dat$Day15_id50=scale(dat$Day15pseudoneutid50_BA.4.BA.5,scale=F)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.2190 1.2448 0.0811 2.700 0.00694
## naive 0.4651 1.5921 0.2136 2.177 0.02946
## D1_id50 -0.9630 0.3818 0.2799 -3.441 0.00058
## Day15_id50 -0.4177 0.6586 0.2126 -1.964 0.04948
## I(Day15_id50^2) -0.4346 0.6475 0.1563 -2.779 0.00545
## naive:D1_id50 0.6896 1.9930 0.2910 2.370 0.01779
##
## Likelihood ratio test=106.1 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213# filter out those with undetectable D1
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*D1_id50 + Day15_id50 + I(Day15_id50^2), subset(dat, D1_id50 > -1.06))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = subset(dat, D1_id50 > -1.06))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.0757 1.0786 0.1045 0.724 0.4688
## naive 0.4559 1.5776 0.3107 1.467 0.1423
## D1_id50 -0.9408 0.3903 0.4848 -1.941 0.0523
## Day15_id50 -0.6463 0.5240 0.2964 -2.180 0.0292
## I(Day15_id50^2) -0.6404 0.5271 0.3936 -1.627 0.1037
## naive:D1_id50 0.8220 2.2751 0.5404 1.521 0.1282
##
## Likelihood ratio test=63.51 on 6 df, p=8.688e-12
## n= 699, number of events= 117# fit model to get inference for D1_id50 in the naive
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + I(1-naive)*D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + I(1 - naive) * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.2190 1.2448 0.0811 2.700 0.00694
## I(1 - naive) -0.4651 0.6281 0.2136 -2.177 0.02946
## D1_id50 -0.2733 0.7608 0.1396 -1.958 0.05027
## Day15_id50 -0.4177 0.6586 0.2126 -1.964 0.04948
## I(Day15_id50^2) -0.4346 0.6475 0.1563 -2.779 0.00545
## I(1 - naive):D1_id50 -0.6896 0.5018 0.2910 -2.370 0.01779
##
## Likelihood ratio test=106.1 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213# filter out those with undetectable D1
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + I(1-naive)*D1_id50 + Day15_id50 + I(Day15_id50^2), subset(dat, D1_id50 > -1.06))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + I(1 - naive) * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = subset(dat, D1_id50 > -1.06))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.0757 1.0786 0.1045 0.724 0.4688
## I(1 - naive) -0.4559 0.6339 0.3107 -1.467 0.1423
## D1_id50 -0.1188 0.8880 0.3228 -0.368 0.7128
## Day15_id50 -0.6463 0.5240 0.2964 -2.180 0.0292
## I(Day15_id50^2) -0.6404 0.5271 0.3936 -1.627 0.1037
## I(1 - naive):D1_id50 -0.8220 0.4395 0.5404 -1.521 0.1282
##
## Likelihood ratio test=63.51 on 6 df, p=8.688e-12
## n= 699, number of events= 117The effect of risk score is different between models with and without double naive (naive and with undetectable D1_id50).
dat$D1_id50=scale(dat$Bpseudoneutid50_BA.4.BA.5,scale=F)
myboxplot(standardized_risk_score~I(1-naive)*I(D1_id50 > -1.06), dat, ylab="standardized_risk_score", names=c("N, D1 undet", "NN, D1 undet", "N, D1 det", "NN, D1 det"))
fit=lm(standardized_risk_score ~ I(1-naive) * I(D1_id50 > -1.06), dat); summary(fit)##
## Call:
## lm(formula = standardized_risk_score ~ I(1 - naive) * I(D1_id50 >
## -1.06), data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5176 -0.5421 0.1354 0.6626 2.0105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03930 0.06026 0.652 0.514516
## I(1 - naive) 0.58424 0.29421 1.986 0.047336 *
## I(D1_id50 > -1.06)TRUE 0.13356 0.08022 1.665 0.096237 .
## I(1 - naive):I(D1_id50 > -1.06)TRUE -1.15269 0.30374 -3.795 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9976 on 981 degrees of freedom
## Multiple R-squared: 0.06359, Adjusted R-squared: 0.06073
## F-statistic: 22.21 on 3 and 981 DF, p-value: 6.459e-14fit=lm(standardized_risk_score ~ I(1-naive), subset(dat, D1_id50 <= -1.06)); summary(fit)##
## Call:
## lm(formula = standardized_risk_score ~ I(1 - naive), data = subset(dat,
## D1_id50 <= -1.06))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2614 -0.4698 0.1859 0.5916 1.9658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0393 0.0567 0.693 0.4888
## I(1 - naive) 0.5842 0.2768 2.111 0.0357 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9385 on 284 degrees of freedom
## Multiple R-squared: 0.01545, Adjusted R-squared: 0.01198
## F-statistic: 4.455 on 1 and 284 DF, p-value: 0.03566f=Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ FOIstandardized + standardized_risk_score
dat.ocp2=subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"Wildtype/Prototype (Pfizer 1)"
, "Omicron (Pfizer 1)"
, "Omicron + Wildtype/Prototype (Pfizer 1)"
# , "Omicron BA.4/5 + Prototype (Pfizer 2)"
# , "Omicron BA.1 + Prototype (Pfizer 2)"
)
)
dat.ocp2$Trt=ifelse(dat.ocp2$treatment_assigned == "Wildtype/Prototype (Pfizer 1)", 0, 1)
coxph(update(f, ~. + naive + Trt), dat.ocp2)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp2)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1126 0.8935 0.2984 -0.377 0.70591
## standardized_risk_score 0.2795 1.3224 0.2878 0.971 0.33154
## naive 0.8310 2.2955 0.4551 1.826 0.06787
## Trt -1.1399 0.3199 0.3749 -3.040 0.00236
##
## Likelihood ratio test=12.14 on 4 df, p=0.01631
## n= 151, number of events= 30coxph(update(f, ~. + naive + Trt), subset(dat.ocp2, naive==0))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp2,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.59531 1.81359 0.61480 0.968 0.333
## standardized_risk_score 0.04664 1.04775 0.52826 0.088 0.930
## naive NA NA 0.00000 NA NA
## Trt -0.52997 0.58862 0.77255 -0.686 0.493
##
## Likelihood ratio test=1.38 on 3 df, p=0.7098
## n= 53, number of events= 7coxph(update(f, ~. + naive + Trt), subset(dat.ocp2, naive==1))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp2,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3243 0.7231 0.3318 -0.977 0.3285
## standardized_risk_score 0.4130 1.5113 0.3592 1.150 0.2503
## naive NA NA 0.0000 NA NA
## Trt -1.3346 0.2633 0.4359 -3.062 0.0022
##
## Likelihood ratio test=10.59 on 3 df, p=0.01415
## n= 98, number of events= 23dat.1=subset(dat.ocp2, naive==1)B and D15 marker
table(dat$Bpseudoneutid50_MDWcat, dat$Day15pseudoneutid50_MDWcat)##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 228 80 20
## (2.56,3.27] 94 155 80
## (3.27, Inf] 6 94 228corplot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, dat, col=ifelse(dat$naive==1,1,2))
# fit demming regression
dat.n=subset(dat, naive==1)
dat.nn=subset(dat, naive==0)
fit.n = Deming(dat.n$Bpseudoneutid50_MDW, dat.n$Day15pseudoneutid50_MDW, boot = TRUE)
fit.nn = Deming(dat.nn$Bpseudoneutid50_MDW, dat.nn$Day15pseudoneutid50_MDW, boot = TRUE)
summary(fit.n)## est se(est) (lower upper)
## Intercept 1.7871909 0.09110302 1.6060820 1.9576747
## Slope 0.7210016 0.03387975 0.6587173 0.7917871
## sigma.dat.n$Bpseudoneutid50_MDW 0.2985262 0.01199177 0.2758940 0.3226616
## sigma.dat.n$Day15pseudoneutid50_MDW 0.2985262 0.01199177 0.2758940 0.3226616
## p.value
## Intercept 1.101506e-85
## Slope 1.695729e-100
## sigma.dat.n$Bpseudoneutid50_MDW 8.584826e-137
## sigma.dat.n$Day15pseudoneutid50_MDW 8.584826e-137summary(fit.nn)## est se(est) (lower upper)
## Intercept 1.5126849 0.23922704 1.0462625 1.9694045
## Slope 0.7497295 0.06739409 0.6212233 0.8797335
## sigma.dat.nn$Bpseudoneutid50_MDW 0.2761024 0.01648200 0.2426575 0.3060327
## sigma.dat.nn$Day15pseudoneutid50_MDW 0.2761024 0.01648200 0.2426575 0.3060327
## p.value
## Intercept 2.561708e-10
## Slope 9.527661e-29
## sigma.dat.nn$Bpseudoneutid50_MDW 5.497849e-63
## sigma.dat.nn$Day15pseudoneutid50_MDW 5.497849e-63par(mfrow=c(2,1), mar=c(3,4,2,2))
lim=range(c(dat$Bpseudoneutid50_MDW, dat$Day15pseudoneutid50_MDW))
corplot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, dat.n, add.deming.fit = T, xlim=lim, ylim=lim, xlab="", main="Naive", method="pearson")
corplot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, dat.nn, add.deming.fit = T, xlim=lim, ylim=lim, main="Non-naive", method="pearson")
title(xlab="Bpseudoneutid50_MDW", line=2)
distribution
dat = subset(dat_proc, ph1.D15 & TrtonedosemRNA==1)
table(dat$Bpseudoneutid50_MDWcat, dat$Day15pseudoneutid50_MDWcat)##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 228 80 20
## (2.56,3.27] 94 155 80
## (3.27, Inf] 6 94 228table(dat$Bpseudoneutid50_MDWcat, dat$Day15pseudoneutid50_MDWcat, dat$naive)## , , = 0
##
##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 11 6 3
## (2.56,3.27] 24 42 26
## (3.27, Inf] 5 67 172
##
## , , = 1
##
##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 217 74 17
## (2.56,3.27] 70 113 54
## (3.27, Inf] 1 27 56# all cases have covid lineage observed
mytable(dat_proc$COVIDlineageObserved, dat_proc$COVIDlineage)##
## BA.2 BA.4 BA.5 XBB.1.5 XBB<ca>0.10 XZ <NA>
## FALSE 23 0 83 13 0 0 0
## TRUE 62 24 161 15 1 1 0
## <NA> 0 0 0 0 0 0 857mytable(dat_proc$COVIDlineageObserved, dat_proc$COVIDIndD22toend)##
## 0 1 <NA>
## FALSE 36 83 0
## TRUE 60 180 24
## <NA> 857 0 0save the risk score from covail_data_processed_20240205.csv as a separate file that will be treated as the source of risk score
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240205.csv")
write.csv(subset(dat1, select=c(Ptid, risk_score, standardized_risk_score)), row.names=F, file="/trials/covpn/COVAILcorrelates/analysis/correlates/adata/risk_score.csv")There is a difference in risk score between 0205 and 0206 analysis ready datasets
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240205.csv")
dat2=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240206.csv")
nrow(dat1)
nrow(dat2)
cbind(dat1$COVIDIndD22toD181, dat2$COVIDIndD22toD181)[1:100,]
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240205.csv")
dat2=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240206.csv")
dat3=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240211.csv")
nrow(dat1)
nrow(dat2)
nrow(dat3)
with(subset(dat1,ph1.D15==1), fastauc(risk_score, COVIDIndD22toD181))
with(subset(dat2,ph1.D15==1), fastauc(risk_score, COVIDIndD22toD181))
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240208.csv")
dat2=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240211.csv")
rbind(dat1[16,-182],dat2[16,]) # COVIDtimeD22toend
setdiff(names(dat1), names(dat2))
mytable(dat2$COVIDIndD92toD181, dat2$COVIDtimeD92toD181)sanofi markers
with(dat_mapped, table(treatment_actual %in% c("Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)"), TrtSanofi))## TrtSanofi
## 0 1
## FALSE 1110 0
## TRUE 0 152dat.sanofi = subset(dat_mapped, TrtSanofi==1) #treatment_actual %in% c("Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)")
summary(dat.sanofi[,c("Day29"%.%assays[1:5])])## Day29pseudoneutid50_D614G Day29pseudoneutid50_Delta Day29pseudoneutid50_Beta
## Min. :2.964 Min. :2.761 Min. :2.450
## 1st Qu.:3.747 1st Qu.:3.542 1st Qu.:3.413
## Median :4.127 Median :3.846 Median :3.813
## Mean :4.118 Mean :3.892 Mean :3.821
## 3rd Qu.:4.457 3rd Qu.:4.260 3rd Qu.:4.238
## Max. :5.195 Max. :5.036 Max. :5.136
## NA's :6 NA's :6 NA's :6
## Day29pseudoneutid50_BA.1 Day29pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:2.769 1st Qu.:2.693
## Median :3.322 Median :3.155
## Mean :3.226 Mean :3.091
## 3rd Qu.:3.720 3rd Qu.:3.543
## Max. :4.865 Max. :4.507
## NA's :6 NA's :6summary(dat.sanofi[,c("Day15"%.%assays[1:5])])## Day15pseudoneutid50_D614G Day15pseudoneutid50_Delta Day15pseudoneutid50_Beta
## Min. :2.830 Min. :2.537 Min. :2.290
## 1st Qu.:3.657 1st Qu.:3.325 1st Qu.:3.257
## Median :3.922 Median :3.612 Median :3.611
## Mean :3.971 Mean :3.657 Mean :3.633
## 3rd Qu.:4.278 3rd Qu.:3.982 3rd Qu.:4.028
## Max. :4.929 Max. :4.733 Max. :4.913
## NA's :6 NA's :6 NA's :6
## Day15pseudoneutid50_BA.1 Day15pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:2.732 1st Qu.:2.618
## Median :3.191 Median :3.021
## Mean :3.204 Mean :3.002
## 3rd Qu.:3.612 3rd Qu.:3.489
## Max. :4.612 Max. :4.493
## NA's :6 NA's :6with(subset(dat_mapped, TrtSanofi==1), mytable(naive, COVIDIndD36toD181))## COVIDIndD36toD181
## naive 0 1 <NA>
## 0 56 4 2
## 1 66 13 11with(subset(dat_mapped, TrtSanofi==1), mytable(naive, COVIDIndD22toD181))## COVIDIndD22toD181
## naive 0 1 <NA>
## 0 56 4 2
## 1 65 18 7Correlation is high between BA4BA5 ID50 and ancestral ID50 in both naive and nnaive (higher in the latter)
par(mfrow=c(1,2))
lim=c(2.5,5.3)
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1), asp=1, xlim=lim, ylim=lim, main="COVAIL Sanofi Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, dat.sanofi, asp=1, xlim=lim, ylim=lim, main="COVAIL Sanofi")
# corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==0), asp=1, xlim=lim, ylim=lim)Comparing with Pfz,P in the naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==1, Day15pseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Day15pseudoneutid50_D614G, drop=T)), test="w", main="D15 Ancestral ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==1, Day15pseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Day15pseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D15 BA4BA5 ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1), asp=1, xlim=lim, ylim=lim, main="Sanofi Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Naive")
Comparing with Pfz,P in the naive, separately by Sanofi,P and Sanofi,NP
par(mfrow=c(1,3))
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim, ylim=lim, main="Sanofi,P Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)"), asp=1, xlim=lim, ylim=lim, main="Sanofi,NP Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Naive")
Comparing with Pfz,P in the non-naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==0, Day15pseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Day15pseudoneutid50_D614G, drop=T)), test="w", main="D15 Ancestral ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==0, Day15pseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Day15pseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D15 BA4BA5 ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==0), asp=1, xlim=lim, ylim=lim, main="Sanofi Non-naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Non-naive")
Baseline correlation, in the naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==1, Bpseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Bpseudoneutid50_D614G, drop=T)), test="w", main="D1 Ancestral ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==1, Bpseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Bpseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D1 BA4BA5 ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.sanofi, naive==1), asp=1, xlim=lim, ylim=lim, main="Sanofi Naive")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Bpseudoneutid50_D614G, Bpseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Naive")
Baseline correlation, in the non-naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==0, Bpseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Bpseudoneutid50_D614G, drop=T)), test="w", main="D1 Ancestral ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==0, Bpseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Bpseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D1 BA4BA5 ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.sanofi, naive==0), asp=1, xlim=lim, ylim=lim, main="Sanofi Non-naive")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0, c(Bpseudoneutid50_D614G, Bpseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Non-naive")
Interaction plot, naive
par(mfrow=c(1,2))
lim=c(1.2,4.8)
myboxplot(subset(dat.sanofi, naive==1, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), names=c("ancestral", "BA4BA5"), main="Sanofi, naive", add.interaction=T)## category.var = x
## names(dat) = x y zmyboxplot(subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), names=c("ancestral", "BA4BA5"), main="Pfz,P, naive", add.interaction=T)
## category.var = x
## names(dat) = x y zCorrelation of fold change between ancestral and BA4BA5
dat.sanofi$Day15overBpseudoneutid50_BA.4.BA.5 = dat.sanofi$Day15pseudoneutid50_BA.4.BA.5 - dat.sanofi$Bpseudoneutid50_BA.4.BA.5
dat.sanofi$Day15overBpseudoneutid50_D614G = dat.sanofi$Day15pseudoneutid50_D614G - dat.sanofi$Bpseudoneutid50_D614G
dat.ocp$Day15overBpseudoneutid50_BA.4.BA.5 = dat.ocp$Day15pseudoneutid50_BA.4.BA.5 - dat.ocp$Bpseudoneutid50_BA.4.BA.5
dat.ocp$Day15overBpseudoneutid50_D614G = dat.ocp$Day15pseudoneutid50_D614G - dat.ocp$Bpseudoneutid50_D614G
dat.sanofi$Day29overBpseudoneutid50_BA.4.BA.5 = dat.sanofi$Day29pseudoneutid50_BA.4.BA.5 - dat.sanofi$Bpseudoneutid50_BA.4.BA.5
dat.sanofi$Day29overBpseudoneutid50_D614G = dat.sanofi$Day29pseudoneutid50_D614G - dat.sanofi$Bpseudoneutid50_D614G
dat.ocp$Day29overBpseudoneutid50_BA.4.BA.5 = dat.ocp$Day29pseudoneutid50_BA.4.BA.5 - dat.ocp$Bpseudoneutid50_BA.4.BA.5
dat.ocp$Day29overBpseudoneutid50_D614G = dat.ocp$Day29pseudoneutid50_D614G - dat.ocp$Bpseudoneutid50_D614GCorrelation between D15 over D1 fold change in ancestral and BA4BA5 ID50.
par(mfrow=c(1,3))
lim1=c(0,3.3)
corplot(Day15overBpseudoneutid50_BA.4.BA.5~Day15overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Prototype Naive")
corplot(Day15overBpseudoneutid50_BA.4.BA.5~Day15overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)" & Day15overBpseudoneutid50_D614G<3), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Beta-containing Naive")
corplot(Day15overBpseudoneutid50_BA.4.BA.5~Day15overBpseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0), asp=1, xlim=lim1, ylim=lim1, main="Pfz,P, naive")
D29 over D1
par(mfrow=c(1,3))
lim1=c(0,3.3)
corplot(Day29overBpseudoneutid50_BA.4.BA.5~Day29overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Prototype Naive")
corplot(Day29overBpseudoneutid50_BA.4.BA.5~Day29overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)" & Day29overBpseudoneutid50_D614G<3), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Beta-containing Naive")
corplot(Day29overBpseudoneutid50_BA.4.BA.5~Day29overBpseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0), asp=1, xlim=lim1, ylim=lim1, main="Pfz,P, naive")
comparing fold change between different Sanofi arms
par(mfrow=c(1,2))
myboxplot(list(
subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)", Day15overBpseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)", Day15overBpseudoneutid50_BA.4.BA.5, drop=T)), test="w", names=c("Prototype", "Non-prototype"))
myboxplot(list(
subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)", Day15overBpseudoneutid50_D614G, drop=T),
subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)", Day15overBpseudoneutid50_D614G, drop=T)), test="w", names=c("Prototype", "Non-prototype"))
Comparing D29 and D15. Quite comparable
par(mfrow=c(1,2))
lim1=NULL
corplot(Day29pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_BA.4.BA.5, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi,P Naive")
corplot(Day29pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_BA.4.BA.5, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi,NP Naive")
correlation between m15 and baseline markers
dat=subset(dat_proc, ph1.D15 & TrtonedosemRNA==1)
mypairs(dat[,paste0("B",assays)])
mypairs(dat[,paste0("Day15",assays)])
mypairs(dat[,paste0(c("B","Day15", "Delta15overB"),assays[1])])
marker missingness
table(dat_proc$AsympInfectIndD15to29, dat_proc$ph1.D15)##
## 0 1
## 0 110 1116
## 1 3 11table(dat_proc$arm, dat_proc$ph1.D15)##
## 0 1
## 1 1 96
## 2 3 109
## 3 85 0
## 4 2 98
## 5 3 96
## 6 4 92
## 7 0 47
## 8 1 50
## 9 1 52
## 10 2 49
## 11 2 49
## 12 0 52
## 13 1 45
## 14 2 48
## 15 1 49
## 16 2 98
## 17 3 97table(dat_mapped$ph1.D15, dat_mapped$Immunemarkerset, dat_mapped$arm==3)## , , = FALSE
##
##
## 0 1
## 0 49 0
## 1 0 1127
##
## , , = TRUE
##
##
## 0 1
## 0 1 85
## 1 0 0# across assays, all or none
summary(subset(dat_mapped, ph1.D15==1)["Day15"%.%assays[1:5]])## Day15pseudoneutid50_D614G Day15pseudoneutid50_Delta Day15pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:4.039 1st Qu.:3.739 1st Qu.:3.643
## Median :4.348 Median :4.069 Median :4.035
## Mean :4.311 Mean :4.050 Mean :3.999
## 3rd Qu.:4.627 3rd Qu.:4.396 3rd Qu.:4.414
## Max. :5.666 Max. :5.381 Max. :5.592
##
## Day15pseudoneutid50_BA.1 Day15pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.238 1st Qu.:2.954
## Median :3.636 Median :3.379
## Mean :3.615 Mean :3.335
## 3rd Qu.:4.050 3rd Qu.:3.786
## Max. :5.225 Max. :5.361
## NA's :666summary(subset(dat_mapped, ph1.D15==1)["Day29"%.%assays[1:5]])## Day29pseudoneutid50_D614G Day29pseudoneutid50_Delta Day29pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.985 1st Qu.:3.687 1st Qu.:3.544
## Median :4.281 Median :4.002 Median :3.938
## Mean :4.265 Mean :3.993 Mean :3.912
## 3rd Qu.:4.570 3rd Qu.:4.320 3rd Qu.:4.312
## Max. :5.605 Max. :5.402 Max. :5.414
## NA's :15 NA's :15 NA's :15
## Day29pseudoneutid50_BA.1 Day29pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.114 1st Qu.:2.889
## Median :3.513 Median :3.272
## Mean :3.482 Mean :3.232
## 3rd Qu.:3.894 3rd Qu.:3.649
## Max. :5.326 Max. :4.907
## NA's :16 NA's :15summary(subset(dat_mapped, ph1.D15==1)["Day91"%.%assays[1:5]])## Day91pseudoneutid50_D614G Day91pseudoneutid50_Delta Day91pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.768 1st Qu.:3.474 1st Qu.:3.337
## Median :4.115 Median :3.818 Median :3.729
## Mean :4.100 Mean :3.807 Mean :3.724
## 3rd Qu.:4.457 3rd Qu.:4.164 3rd Qu.:4.170
## Max. :5.624 Max. :5.438 Max. :5.420
## NA's :27 NA's :27 NA's :27
## Day91pseudoneutid50_BA.1 Day91pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:2.937 1st Qu.:2.715
## Median :3.396 Median :3.169
## Mean :3.337 Mean :3.121
## 3rd Qu.:3.779 3rd Qu.:3.611
## Max. :5.190 Max. :4.975
## NA's :27 NA's :27summary(subset(dat_mapped, ph1.D15==1)["Day181"%.%assays[1:5]])## Day181pseudoneutid50_D614G Day181pseudoneutid50_Delta
## Min. :1.301 Min. :1.301
## 1st Qu.:3.639 1st Qu.:3.282
## Median :3.980 Median :3.646
## Mean :3.960 Mean :3.617
## 3rd Qu.:4.338 3rd Qu.:3.970
## Max. :5.601 Max. :5.118
## NA's :59 NA's :59
## Day181pseudoneutid50_Beta Day181pseudoneutid50_BA.1
## Min. :1.301 Min. :1.301
## 1st Qu.:3.135 1st Qu.:2.852
## Median :3.602 Median :3.286
## Mean :3.551 Mean :3.229
## 3rd Qu.:3.997 3rd Qu.:3.738
## Max. :5.298 Max. :5.228
## NA's :59 NA's :59
## Day181pseudoneutid50_BA.4.BA.5
## Min. :1.301
## 1st Qu.:2.603
## Median :3.124
## Mean :3.056
## 3rd Qu.:3.591
## Max. :4.913
## NA's :59summary(subset(dat_proc, ph1.D15 & is.na(Day29pseudoneutid50_D614G))["Day29"%.%assays[1:5]])## Day29pseudoneutid50_D614G Day29pseudoneutid50_Delta Day29pseudoneutid50_Beta
## Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NA's :15 NA's :15 NA's :15
## Day29pseudoneutid50_BA.1 Day29pseudoneutid50_BA.4.BA.5
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :15 NA's :15summary(subset(dat_proc, ph1.D15 & is.na(Day91pseudoneutid50_D614G))["Day91"%.%assays[1:5]])## Day91pseudoneutid50_D614G Day91pseudoneutid50_Delta Day91pseudoneutid50_Beta
## Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NA's :27 NA's :27 NA's :27
## Day91pseudoneutid50_BA.1 Day91pseudoneutid50_BA.4.BA.5
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :27 NA's :27summary(subset(dat_proc, ph1.D15 & is.na(Day181pseudoneutid50_D614G))["Day181"%.%assays[1:5]])## Day181pseudoneutid50_D614G Day181pseudoneutid50_Delta
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :59 NA's :59
## Day181pseudoneutid50_Beta Day181pseudoneutid50_BA.1
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :59 NA's :59
## Day181pseudoneutid50_BA.4.BA.5
## Min. : NA
## 1st Qu.: NA
## Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## NA's :59dat_proc$kp = dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to271!=1
dat_proc$kp = dat_proc$ph1.D15==1
for (i in 1:1) {
with(dat_proc[dat_proc$kp,], print(table(!is.na(get("Day29"%.%assays[i])), !is.na(get("Day15"%.%assays[i])))))
with(dat_proc[dat_proc$kp,], print(table(!is.na(get("Day91"%.%assays[i])), !is.na(get("Day15"%.%assays[i])))))
with(dat_proc[dat_proc$kp,], print(table(!is.na(get("Day181"%.%assays[i])), !is.na(get("Day15"%.%assays[i])))))
}##
## TRUE
## FALSE 15
## TRUE 1112
##
## TRUE
## FALSE 27
## TRUE 1100
##
## TRUE
## FALSE 59
## TRUE 1068myboxplot(
list(dat_proc$Day15pseudoneutid50_MDW[dat_proc$treatment_actual=="Omicron + Wildtype/Prototype (Pfizer 1)"],
dat_proc$Day15pseudoneutid50_MDW[dat_proc$treatment_actual=="1 Dose Omicron + Prototype (Moderna)"])
)
myboxplot(
list(dat_proc$Day15pseudoneutid50_D614G[dat_proc$treatment_actual=="Omicron + Wildtype/Prototype (Pfizer 1)"],
dat_proc$Day15pseudoneutid50_D614G[dat_proc$treatment_actual=="1 Dose Omicron + Prototype (Moderna)"])
)
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
# x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
#
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
# x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
#
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
# x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
#
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
# x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
# marker level over time
par(mfrow=c(4,1), mar=c(2,4,1,1))
dat_proc$ph1 = dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to181!=1 & dat_proc$AsympInfectIndD182to271!=1
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
dat_proc$ph1 = dat_proc$ph1.D92 & dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to181!=1 & dat_proc$AsympInfectIndD182to271!=1
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL) 
sum(dat_proc$ph1.D15==1)## [1] 1127dat_proc$kp = dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to271!=1 &
!is.na(dat_proc$Day29pseudoneutid50_D614G) & !is.na(dat_proc$Day91pseudoneutid50_D614G) & !is.na(dat_proc$Day181pseudoneutid50_D614G)
nrow(subset(dat_proc, kp))## [1] 733nrow(subset(dat_proc, kp & Bpseudoneutid50_BA.1>1.5))## [1] 619nrow(subset(dat_proc, kp & Bpseudoneutid50_BA.1<1.5))## [1] 114Misc
myboxplot(dat_mapped[, c("B"%.%assays[1:5], "Day15"%.%assays[1:5])], names=sub("pseudoneutid50_", "", rep(assays[1:5],2)))
summary(dat_mapped)## Subjectid EthnicityHispanic EthnicityNotreported race
## Length:1262 Min. :0.00000 Min. :0.0000000 Length:1262
## Class :character 1st Qu.:0.00000 1st Qu.:0.0000000 Class :character
## Mode :character Median :0.00000 Median :0.0000000 Mode :character
## Mean :0.09113 Mean :0.0007924
## 3rd Qu.:0.00000 3rd Qu.:0.0000000
## Max. :1.00000 Max. :1.0000000
##
## Asian Black NatAmer PacIsl
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.000000
## Mean :0.1173 Mean :0.09033 Mean :0.01426 Mean :0.001585
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.000000
##
## White Multiracial Unknown Age
## Min. :0.0000 Min. :0.00000 Min. :0.000000 Min. :18.00
## 1st Qu.:1.0000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:33.00
## Median :1.0000 Median :0.00000 Median :0.000000 Median :45.00
## Mean :0.8035 Mean :0.03487 Mean :0.009509 Mean :47.66
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:65.00
## Max. :1.0000 Max. :1.00000 Max. :1.000000 Max. :85.00
##
## Age65C State City Sex
## Min. :0.0000 Length:1262 Length:1262 Min. :0.0000
## 1st Qu.:0.0000 Class :character Class :character 1st Qu.:0.0000
## Median :0.0000 Mode :character Mode :character Median :1.0000
## Mean :0.2647 Mean :0.5452
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
##
## naive eligibility_deviation early_term_date oos_boost_date
## Min. :0.0000 Length:1262 Length:1262 Length:1262
## 1st Qu.:0.0000 Class :character Class :character Class :character
## Median :1.0000 Mode :character Mode :character Mode :character
## Mean :0.6513
## 3rd Qu.:1.0000
## Max. :1.0000
##
## RESULT RESULT_limit UNITS ASSAY
## Min. : 41.0 Length:1262 Length:1262 Length:1262
## 1st Qu.: 251.5 Class :character Class :character Class :character
## Median : 646.0 Mode :character Mode :character Mode :character
## Mean : 2117.9
## 3rd Qu.: 2093.8
## Max. :48003.0
## NA's :238
## stage treatment_actual treatment_assigned stratalab
## Min. :1.000 Length:1262 Length:1262 Length:1262
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :2.000 Mode :character Mode :character Mode :character
## Mean :1.966
## 3rd Qu.:3.000
## Max. :4.000
##
## primary_vax1_date primary_vax2_date pre_study_booster_date
## Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## primary_booster_type pre_study_infect_date studydose1date
## Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## studydose2date early_term oos_boost infect_date1
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## infect_date2 symptomatic_infect1 symptomatic_infect2 NAntibody
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CTSTAT_SWAB1 CTVALUE_SWAB1 ASSSTAT_SWAB1 ASSQC_SWAB1
## Min. :1 Min. :14.00 Min. :1.000 Length:1262
## 1st Qu.:1 1st Qu.:19.70 1st Qu.:1.000 Class :character
## Median :1 Median :22.40 Median :1.000 Mode :character
## Mean :1 Mean :22.80 Mean :1.022
## 3rd Qu.:1 3rd Qu.:25.75 3rd Qu.:1.000
## Max. :1 Max. :32.80 Max. :2.000
## NA's :983 NA's :983 NA's :983
## TOTREADS_SWAB1 COVRDPCT_SWAB1 HSTRDPCT_SWAB1 BACRDPCT_SWAB1
## Min. : 38148 Min. : 5.64 Min. : 0.000 Min. : 0.0000
## 1st Qu.:239706 1st Qu.:95.51 1st Qu.: 0.040 1st Qu.: 0.0600
## Median :317509 Median :97.23 Median : 0.140 Median : 0.1100
## Mean :312744 Mean :95.57 Mean : 1.228 Mean : 0.3963
## 3rd Qu.:383596 3rd Qu.:98.36 3rd Qu.: 0.905 3rd Qu.: 0.3650
## Max. :764242 Max. :99.22 Max. :29.430 Max. :40.7300
## NA's :983 NA's :983 NA's :983 NA's :983
## ASSCOMP_SWAB1 PANGOLIN_SWAB1 PANGOVER_SWAB1 SCORCALL_SWAB1
## Min. :0.7400 Length:1262 Length:1262 Length:1262
## 1st Qu.:0.9900 Class :character Class :character Class :character
## Median :0.9900 Mode :character Mode :character Mode :character
## Mean :0.9834
## 3rd Qu.:0.9900
## Max. :0.9900
## NA's :983
## SCORVER_SWAB1 TRUNCLIN_SWAB1 TRUNCVER_SWAB1 SPKAASUB_SWAB1
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## SPKAADEL_SWAB1 SPKAAINS_SWAB1 DATE_COLLECT_SWAB1 CTSTAT_SWAB2
## Length:1262 Mode:logical Length:1262 Min. :1
## Class :character NA's:1262 Class :character 1st Qu.:1
## Mode :character Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :1261
## CTVALUE_SWAB2 ASSSTAT_SWAB2 ASSQC_SWAB2 TOTREADS_SWAB2
## Min. :24.8 Min. :1 Length:1262 Min. :94834
## 1st Qu.:24.8 1st Qu.:1 Class :character 1st Qu.:94834
## Median :24.8 Median :1 Mode :character Median :94834
## Mean :24.8 Mean :1 Mean :94834
## 3rd Qu.:24.8 3rd Qu.:1 3rd Qu.:94834
## Max. :24.8 Max. :1 Max. :94834
## NA's :1261 NA's :1261 NA's :1261
## COVRDPCT_SWAB2 HSTRDPCT_SWAB2 BACRDPCT_SWAB2 ASSCOMP_SWAB2
## Min. :97.38 Min. :0.71 Min. :0.61 Min. :0.99
## 1st Qu.:97.38 1st Qu.:0.71 1st Qu.:0.61 1st Qu.:0.99
## Median :97.38 Median :0.71 Median :0.61 Median :0.99
## Mean :97.38 Mean :0.71 Mean :0.61 Mean :0.99
## 3rd Qu.:97.38 3rd Qu.:0.71 3rd Qu.:0.61 3rd Qu.:0.99
## Max. :97.38 Max. :0.71 Max. :0.61 Max. :0.99
## NA's :1261 NA's :1261 NA's :1261 NA's :1261
## PANGOLIN_SWAB2 PANGOVER_SWAB2 SCORCALL_SWAB2 SCORVER_SWAB2
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TRUNCLIN_SWAB2 TRUNCVER_SWAB2 SPKAASUB_SWAB2 SPKAADEL_SWAB2
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## SPKAAINS_SWAB2 DATE_COLLECT_SWAB2 TrtmRNA arm
## Mode:logical Length:1262 Min. :0.0000 Min. : 1.000
## NA's:1262 Class :character 1st Qu.:1.0000 1st Qu.: 4.000
## Mode :character Median :1.0000 Median : 7.000
## Mean :0.8796 Mean : 8.315
## 3rd Qu.:1.0000 3rd Qu.:13.000
## Max. :1.0000 Max. :17.000
##
## TrtA TrtB TrtC TrtonedosemRNA
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :0.0000 Median :1.0000 Median :1.0000
## Mean :0.6624 Mean :0.1618 Mean :0.8018 Mean :0.8114
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :643 NA's :341 NA's :490
## TrtSanofi pre_study_booster_until_studydose1_day
## Min. :0.0000 Min. : 11.0
## 1st Qu.:0.0000 1st Qu.:164.0
## Median :0.0000 Median :191.0
## Mean :0.1204 Mean :207.7
## 3rd Qu.:0.0000 3rd Qu.:230.0
## Max. :1.0000 Max. :592.0
##
## pre_study_booster_until_studydose1_day_median
## Min. :191
## 1st Qu.:191
## Median :191
## Mean :191
## 3rd Qu.:191
## Max. :191
##
## pre_study_booster_until_studydose1_ind D1_D15_flag D1_D29_flag
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000 Median :1.0000
## Mean :0.4984 Mean :0.9818 Mean :0.9802
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## Bpseudoneutid50_BA.1 Bpseudoneutid50_Beta Bpseudoneutid50_Delta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:2.072 1st Qu.:2.590 1st Qu.:2.904
## Median :2.612 Median :3.130 Median :3.357
## Mean :2.583 Mean :3.072 Mean :3.315
## 3rd Qu.:3.181 3rd Qu.:3.606 3rd Qu.:3.775
## Max. :4.681 Max. :4.917 Max. :4.893
## NA's :1 NA's :1 NA's :1
## Bpseudoneutid50Duke_BA.2.12.1 Bpseudoneutid50_BA.4.BA.5 Bpseudoneutid50_D614G
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:2.041 1st Qu.:1.301 1st Qu.:3.257
## Median :2.394 Median :2.425 Median :3.649
## Mean :2.396 Mean :2.341 Mean :3.636
## 3rd Qu.:2.833 3rd Qu.:2.990 3rd Qu.:4.096
## Max. :4.235 Max. :4.385 Max. :5.084
## NA's :1137 NA's :1 NA's :1
## Day15pseudoneutid50_BA.1 Day15pseudoneutid50_Beta Day15pseudoneutid50_Delta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.239 1st Qu.:3.648 1st Qu.:3.734
## Median :3.631 Median :4.035 Median :4.067
## Mean :3.613 Mean :4.000 Mean :4.044
## 3rd Qu.:4.053 3rd Qu.:4.415 3rd Qu.:4.391
## Max. :5.225 Max. :5.592 Max. :5.381
## NA's :22 NA's :22 NA's :22
## Day15pseudoneutid50Duke_BA.2.12.1 Day15pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.078 1st Qu.:2.952
## Median :3.382 Median :3.380
## Mean :3.390 Mean :3.335
## 3rd Qu.:3.695 3rd Qu.:3.784
## Max. :4.859 Max. :5.361
## NA's :1137 NA's :791
## Day15pseudoneutid50_D614G Day29pseudoneutid50_BA.1 Day29pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:4.035 1st Qu.:3.119 1st Qu.:3.546
## Median :4.343 Median :3.514 Median :3.931
## Mean :4.307 Mean :3.485 Mean :3.913
## 3rd Qu.:4.627 3rd Qu.:3.898 3rd Qu.:4.324
## Max. :5.666 Max. :5.326 Max. :5.414
## NA's :22 NA's :25 NA's :24
## Day29pseudoneutid50_Delta Day29pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.679 1st Qu.:2.889
## Median :3.998 Median :3.267
## Mean :3.988 Mean :3.229
## 3rd Qu.:4.320 3rd Qu.:3.644
## Max. :5.402 Max. :4.907
## NA's :24 NA's :24
## Day29pseudoneutid50_D614G Day91pseudoneutid50_BA.1 Day91pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.968 1st Qu.:2.948 1st Qu.:3.349
## Median :4.278 Median :3.400 Median :3.739
## Mean :4.256 Mean :3.340 Mean :3.731
## 3rd Qu.:4.574 3rd Qu.:3.779 3rd Qu.:4.179
## Max. :5.605 Max. :5.190 Max. :5.420
## NA's :24 NA's :121 NA's :121
## Day91pseudoneutid50_Delta Day91pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.482 1st Qu.:2.719
## Median :3.821 Median :3.187
## Mean :3.815 Mean :3.126
## 3rd Qu.:4.180 3rd Qu.:3.615
## Max. :5.438 Max. :4.975
## NA's :121 NA's :121
## Day91pseudoneutid50_D614G Day181pseudoneutid50_BA.1 Day181pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.772 1st Qu.:2.855 1st Qu.:3.155
## Median :4.122 Median :3.297 Median :3.614
## Mean :4.107 Mean :3.237 Mean :3.561
## 3rd Qu.:4.459 3rd Qu.:3.749 3rd Qu.:4.021
## Max. :5.624 Max. :5.228 Max. :5.298
## NA's :121 NA's :157 NA's :157
## Day181pseudoneutid50_Delta Day181pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.289 1st Qu.:2.612
## Median :3.657 Median :3.154
## Mean :3.623 Mean :3.065
## 3rd Qu.:3.991 3rd Qu.:3.599
## Max. :5.118 Max. :4.913
## NA's :157 NA's :157
## Day181pseudoneutid50_D614G Day85pseudoneutid50_BA.1 Day85pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.656 1st Qu.:3.289 1st Qu.:3.646
## Median :3.993 Median :3.574 Median :4.078
## Mean :3.966 Mean :3.614 Mean :4.012
## 3rd Qu.:4.346 3rd Qu.:4.009 3rd Qu.:4.365
## Max. :5.601 Max. :4.837 Max. :5.144
## NA's :157 NA's :1177 NA's :1177
## Day85pseudoneutid50_Delta Day85pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.710 1st Qu.:2.982
## Median :3.942 Median :3.215
## Mean :3.994 Mean :3.307
## 3rd Qu.:4.365 3rd Qu.:3.709
## Max. :5.082 Max. :4.623
## NA's :1177 NA's :1177
## Day85pseudoneutid50_D614G Day147pseudoneutid50_BA.1 Day147pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.892 1st Qu.:3.030 1st Qu.:3.255
## Median :4.189 Median :3.384 Median :3.721
## Mean :4.213 Mean :3.418 Mean :3.704
## 3rd Qu.:4.557 3rd Qu.:3.886 3rd Qu.:4.092
## Max. :5.179 Max. :4.796 Max. :4.740
## NA's :1177 NA's :1176 NA's :1176
## Day147pseudoneutid50_Delta Day147pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.369 1st Qu.:2.616
## Median :3.694 Median :3.070
## Mean :3.700 Mean :3.078
## 3rd Qu.:4.130 3rd Qu.:3.696
## Max. :4.803 Max. :4.481
## NA's :1176 NA's :1176
## Day147pseudoneutid50_D614G symptm_infect1 symptm_infect1_date
## Min. :1.301 Min. :0.0000 Length:1262
## 1st Qu.:3.630 1st Qu.:0.0000 Class :character
## Median :3.984 Median :0.0000 Mode :character
## Mean :3.999 Mean :0.2979
## 3rd Qu.:4.373 3rd Qu.:1.0000
## Max. :4.963 Max. :1.0000
## NA's :1176
## N_status_1.0 N_status_15.2 N_status_181.14 N_status_271.14
## Min. :0.0000 Min. :0 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0 Median :1.000 Median :1.0000
## Mean :0.3315 Mean :0 Mean :0.552 Mean :0.5795
## 3rd Qu.:1.0000 3rd Qu.:0 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :0 Max. :1.000 Max. :1.0000
## NA's :1 NA's :22 NA's :157 NA's :375
## N_status_29.2 N_status_91.7 Actual_visit_date_1.0 Actual_visit_date_15.2
## Min. :0.0000 Min. :0.0000 Length:1262 Length:1262
## 1st Qu.:0.0000 1st Qu.:0.0000 Class :character Class :character
## Median :0.0000 Median :0.0000 Mode :character Mode :character
## Mean :0.3586 Mean :0.4663
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :24 NA's :121
## Actual_visit_date_181.14 Actual_visit_date_271.14 Actual_visit_date_29.2
## Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Actual_visit_date_91.7 first_Npos_date Target_study_day_numeric
## Length:1262 Length:1262 Min. : 1.00
## Class :character Class :character 1st Qu.: 1.00
## Mode :character Mode :character Median : 1.00
## Mean : 71.81
## 3rd Qu.:181.00
## Max. :271.00
## NA's :497
## NumberdaysD15toD29 NumberdaysD15toD91 NumberdaysD15toD181 last_contact_date
## Min. : 6.00 Min. : 54.00 Min. : 98.0 Length:1262
## 1st Qu.:13.00 1st Qu.: 70.00 1st Qu.:154.0 Class :character
## Median :14.00 Median : 75.00 Median :159.0 Mode :character
## Mean :14.18 Mean : 74.64 Mean :158.9
## 3rd Qu.:15.00 3rd Qu.: 77.00 3rd Qu.:167.0
## Max. :34.00 Max. :102.00 Max. :191.0
## NA's :38 NA's :134 NA's :173
## Perprotocol symptm_infect1_date_imp oos_boost_date_imp
## Min. :0.0000 Length:1262 Length:1262
## 1st Qu.:1.0000 Class :character Class :character
## Median :1.0000 Mode :character Mode :character
## Mean :0.9794
## 3rd Qu.:1.0000
## Max. :1.0000
##
## early_term_date_imp EarlyendpointD15 Immunemarkerset
## Length:1262 Min. :0.00000 Min. :0.0000
## Class :character 1st Qu.:0.00000 1st Qu.:1.0000
## Mode :character Median :0.00000 Median :1.0000
## Mean :0.01936 Mean :0.9604
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.0000
## NA's :22
## ImmunemarkersetD92toD181 ph1.D15 ph1.D92 ph1.D29
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.000 Median :1.0000 Median :0.0000
## Mean :0.7971 Mean :0.893 Mean :0.7393 Mean :0.1086
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
##
## CNSR1_D22toD91 CNSR1_D92toD181 CNSR1_D22toD181 CNSR1_D36toD181
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CNSR1_D22toend CNSR2 CNSR3 CNSR4
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CNSR5 CNSR_D22toD91 CNSR_D92toD181 CNSR_D22toD181
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CNSR_D36toD181 CNSR_D22toend COVIDtimeD22toD91 COVIDIndD22toD91
## Length:1262 Length:1262 Min. :-14.00 Min. :0.0000
## Class :character Class :character 1st Qu.: 91.00 1st Qu.:0.0000
## Mode :character Mode :character Median : 91.00 Median :0.0000
## Mean : 79.48 Mean :0.1332
## 3rd Qu.: 91.00 3rd Qu.:0.0000
## Max. : 91.00 Max. :1.0000
## NA's :22 NA's :46
## COVIDtimeD92toD181 COVIDIndD92toD181 COVIDtimeD22toD181 COVIDIndD22toD181
## Min. :-14.0 Min. :0.00000 Min. :-14.0 Min. :0.0000
## 1st Qu.: 94.0 1st Qu.:0.00000 1st Qu.: 94.0 1st Qu.:0.0000
## Median :171.0 Median :0.00000 Median :171.0 Median :0.0000
## Mean :140.2 Mean :0.07102 Mean :140.2 Mean :0.1941
## 3rd Qu.:188.0 3rd Qu.:0.00000 3rd Qu.:188.0 3rd Qu.:0.0000
## Max. :188.0 Max. :1.00000 Max. :188.0 Max. :1.0000
## NA's :22 NA's :220 NA's :22 NA's :46
## COVIDtimeD36toD181 COVIDIndD36toD181 COVIDIndD22toend AsympInfectIndD15to29
## Min. :-27.0 Min. :0.000 Min. :0.0000 Min. :0.00000
## 1st Qu.: 82.0 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :156.0 Median :0.000 Median :0.0000 Median :0.00000
## Mean :132.3 Mean :0.179 Mean :0.2163 Mean :0.01109
## 3rd Qu.:188.0 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :188.0 Max. :1.000 Max. :1.0000 Max. :1.00000
## NA's :24 NA's :72 NA's :46
## AsympInfectIndD30to91 AsympInfectIndD15to91 AsympInfectIndD92to181
## Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.0000
## Mean :0.01664 Mean :0.02773 Mean :0.0206
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000
##
## AsympInfectIndD182to271 AsympInfectIndD15to181 AsympInfectIndD15to271
## Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.01347 Mean :0.04834 Mean :0.06181
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000
## sort(names(dat_mapped))## [1] "Actual_visit_date_1.0"
## [2] "Actual_visit_date_15.2"
## [3] "Actual_visit_date_181.14"
## [4] "Actual_visit_date_271.14"
## [5] "Actual_visit_date_29.2"
## [6] "Actual_visit_date_91.7"
## [7] "Age"
## [8] "Age65C"
## [9] "arm"
## [10] "Asian"
## [11] "ASSAY"
## [12] "ASSCOMP_SWAB1"
## [13] "ASSCOMP_SWAB2"
## [14] "ASSQC_SWAB1"
## [15] "ASSQC_SWAB2"
## [16] "ASSSTAT_SWAB1"
## [17] "ASSSTAT_SWAB2"
## [18] "AsympInfectIndD15to181"
## [19] "AsympInfectIndD15to271"
## [20] "AsympInfectIndD15to29"
## [21] "AsympInfectIndD15to91"
## [22] "AsympInfectIndD182to271"
## [23] "AsympInfectIndD30to91"
## [24] "AsympInfectIndD92to181"
## [25] "BACRDPCT_SWAB1"
## [26] "BACRDPCT_SWAB2"
## [27] "Black"
## [28] "Bpseudoneutid50_BA.1"
## [29] "Bpseudoneutid50_BA.4.BA.5"
## [30] "Bpseudoneutid50_Beta"
## [31] "Bpseudoneutid50_D614G"
## [32] "Bpseudoneutid50_Delta"
## [33] "Bpseudoneutid50Duke_BA.2.12.1"
## [34] "City"
## [35] "CNSR_D22toD181"
## [36] "CNSR_D22toD91"
## [37] "CNSR_D22toend"
## [38] "CNSR_D36toD181"
## [39] "CNSR_D92toD181"
## [40] "CNSR1_D22toD181"
## [41] "CNSR1_D22toD91"
## [42] "CNSR1_D22toend"
## [43] "CNSR1_D36toD181"
## [44] "CNSR1_D92toD181"
## [45] "CNSR2"
## [46] "CNSR3"
## [47] "CNSR4"
## [48] "CNSR5"
## [49] "COVIDIndD22toD181"
## [50] "COVIDIndD22toD91"
## [51] "COVIDIndD22toend"
## [52] "COVIDIndD36toD181"
## [53] "COVIDIndD92toD181"
## [54] "COVIDtimeD22toD181"
## [55] "COVIDtimeD22toD91"
## [56] "COVIDtimeD36toD181"
## [57] "COVIDtimeD92toD181"
## [58] "COVRDPCT_SWAB1"
## [59] "COVRDPCT_SWAB2"
## [60] "CTSTAT_SWAB1"
## [61] "CTSTAT_SWAB2"
## [62] "CTVALUE_SWAB1"
## [63] "CTVALUE_SWAB2"
## [64] "D1_D15_flag"
## [65] "D1_D29_flag"
## [66] "DATE_COLLECT_SWAB1"
## [67] "DATE_COLLECT_SWAB2"
## [68] "Day147pseudoneutid50_BA.1"
## [69] "Day147pseudoneutid50_BA.4.BA.5"
## [70] "Day147pseudoneutid50_Beta"
## [71] "Day147pseudoneutid50_D614G"
## [72] "Day147pseudoneutid50_Delta"
## [73] "Day15pseudoneutid50_BA.1"
## [74] "Day15pseudoneutid50_BA.4.BA.5"
## [75] "Day15pseudoneutid50_Beta"
## [76] "Day15pseudoneutid50_D614G"
## [77] "Day15pseudoneutid50_Delta"
## [78] "Day15pseudoneutid50Duke_BA.2.12.1"
## [79] "Day181pseudoneutid50_BA.1"
## [80] "Day181pseudoneutid50_BA.4.BA.5"
## [81] "Day181pseudoneutid50_Beta"
## [82] "Day181pseudoneutid50_D614G"
## [83] "Day181pseudoneutid50_Delta"
## [84] "Day29pseudoneutid50_BA.1"
## [85] "Day29pseudoneutid50_BA.4.BA.5"
## [86] "Day29pseudoneutid50_Beta"
## [87] "Day29pseudoneutid50_D614G"
## [88] "Day29pseudoneutid50_Delta"
## [89] "Day85pseudoneutid50_BA.1"
## [90] "Day85pseudoneutid50_BA.4.BA.5"
## [91] "Day85pseudoneutid50_Beta"
## [92] "Day85pseudoneutid50_D614G"
## [93] "Day85pseudoneutid50_Delta"
## [94] "Day91pseudoneutid50_BA.1"
## [95] "Day91pseudoneutid50_BA.4.BA.5"
## [96] "Day91pseudoneutid50_Beta"
## [97] "Day91pseudoneutid50_D614G"
## [98] "Day91pseudoneutid50_Delta"
## [99] "early_term"
## [100] "early_term_date"
## [101] "early_term_date_imp"
## [102] "EarlyendpointD15"
## [103] "eligibility_deviation"
## [104] "EthnicityHispanic"
## [105] "EthnicityNotreported"
## [106] "first_Npos_date"
## [107] "HSTRDPCT_SWAB1"
## [108] "HSTRDPCT_SWAB2"
## [109] "Immunemarkerset"
## [110] "ImmunemarkersetD92toD181"
## [111] "infect_date1"
## [112] "infect_date2"
## [113] "last_contact_date"
## [114] "Multiracial"
## [115] "N_status_1.0"
## [116] "N_status_15.2"
## [117] "N_status_181.14"
## [118] "N_status_271.14"
## [119] "N_status_29.2"
## [120] "N_status_91.7"
## [121] "naive"
## [122] "NAntibody"
## [123] "NatAmer"
## [124] "NumberdaysD15toD181"
## [125] "NumberdaysD15toD29"
## [126] "NumberdaysD15toD91"
## [127] "oos_boost"
## [128] "oos_boost_date"
## [129] "oos_boost_date_imp"
## [130] "PacIsl"
## [131] "PANGOLIN_SWAB1"
## [132] "PANGOLIN_SWAB2"
## [133] "PANGOVER_SWAB1"
## [134] "PANGOVER_SWAB2"
## [135] "Perprotocol"
## [136] "ph1.D15"
## [137] "ph1.D29"
## [138] "ph1.D92"
## [139] "pre_study_booster_date"
## [140] "pre_study_booster_until_studydose1_day"
## [141] "pre_study_booster_until_studydose1_day_median"
## [142] "pre_study_booster_until_studydose1_ind"
## [143] "pre_study_infect_date"
## [144] "primary_booster_type"
## [145] "primary_vax1_date"
## [146] "primary_vax2_date"
## [147] "race"
## [148] "RESULT"
## [149] "RESULT_limit"
## [150] "SCORCALL_SWAB1"
## [151] "SCORCALL_SWAB2"
## [152] "SCORVER_SWAB1"
## [153] "SCORVER_SWAB2"
## [154] "Sex"
## [155] "SPKAADEL_SWAB1"
## [156] "SPKAADEL_SWAB2"
## [157] "SPKAAINS_SWAB1"
## [158] "SPKAAINS_SWAB2"
## [159] "SPKAASUB_SWAB1"
## [160] "SPKAASUB_SWAB2"
## [161] "stage"
## [162] "State"
## [163] "stratalab"
## [164] "studydose1date"
## [165] "studydose2date"
## [166] "Subjectid"
## [167] "symptm_infect1"
## [168] "symptm_infect1_date"
## [169] "symptm_infect1_date_imp"
## [170] "symptomatic_infect1"
## [171] "symptomatic_infect2"
## [172] "Target_study_day_numeric"
## [173] "TOTREADS_SWAB1"
## [174] "TOTREADS_SWAB2"
## [175] "treatment_actual"
## [176] "treatment_assigned"
## [177] "TrtA"
## [178] "TrtB"
## [179] "TrtC"
## [180] "TrtmRNA"
## [181] "TrtonedosemRNA"
## [182] "TrtSanofi"
## [183] "TRUNCLIN_SWAB1"
## [184] "TRUNCLIN_SWAB2"
## [185] "TRUNCVER_SWAB1"
## [186] "TRUNCVER_SWAB2"
## [187] "UNITS"
## [188] "Unknown"
## [189] "White"table(dat_mapped$treatment_actual, dat_mapped$stage)##
## 1 2 3 4
## 1 Dose Beta + Omicron (Moderna) 113 0 0 0
## 1 Dose Delta + Omicron (Moderna) 101 0 0 0
## 1 Dose Omicron (Moderna) 99 0 0 0
## 1 Dose Omicron + Prototype (Moderna) 99 0 0 0
## 1 Dose Prototype (Moderna) 99 0 0 0
## 2 Dose Beta + Omicron (Moderna) 86 0 0 0
## Beta (Pfizer 1) 0 51 0 0
## Beta (Sanofi) 0 0 51 0
## Beta + Omicron (Pfizer 1) 0 52 0 0
## Beta + Prototype (Sanofi) 0 0 52 0
## Beta + Wildtype/Prototype (Pfizer 1) 0 52 0 0
## Omicron (Pfizer 1) 0 54 0 0
## Omicron + Wildtype/Prototype (Pfizer 1) 0 53 0 0
## Omicron BA.1 + Wildtype/Prototype (Pfizer 2) 0 0 0 101
## Omicron BA.4/5 + Wildtype/Prototype (Pfizer 2) 0 0 0 100
## Prototype (Sanofi) 0 0 49 0
## Wildtype/Prototype (Pfizer 1) 0 50 0 0with(subset(dat, ph1.D15==1 & COVIDIndD22toD181), mytable(treatment_actual, COVIDlineageObserved))## COVIDlineageObserved
## treatment_actual FALSE TRUE
## 1 Dose Beta + Omicron (Moderna) 9 28
## 1 Dose Delta + Omicron (Moderna) 5 23
## 1 Dose Omicron (Moderna) 7 18
## 1 Dose Omicron + Prototype (Moderna) 8 18
## 1 Dose Prototype (Moderna) 7 20
## Beta (Pfizer 1) 4 5
## Beta + Omicron (Pfizer 1) 3 4
## Beta + Wildtype/Prototype (Pfizer 1) 2 7
## Omicron (Pfizer 1) 5 3
## Omicron + Wildtype/Prototype (Pfizer 1) 0 6
## Omicron BA.1 + Wildtype/Prototype (Pfizer 2) 3 5
## Omicron BA.4/5 + Wildtype/Prototype (Pfizer 2) 2 5
## Wildtype/Prototype (Pfizer 1) 4 12table(dat_mapped$Immunemarkerset, dat_mapped$Perprotocol, useNA='ifany')##
## 0 1
## 0 26 24
## 1 0 1212table(dat_mapped$Immunemarkerset, dat_mapped$eligibility_deviation, useNA='ifany')##
## Y
## 0 47 3
## 1 1212 0